Abstract
The purpose of this study is to develop and evaluate a mobile-assisted Chinese language learning app that introduces hundreds of fundamental Chinese characters with an innovative method, namely the Key-Image Mnemonics (KIM). This method is developed based on cognitive and learning theories (i.e., the Elaboration Theory, the Dual Coding Theory, and the Key-Word Method) and the effectiveness of this method in Chinese-as-foreign/second-language (CFL/CSL) learning is strongly supported by laboratory and classroom studies. In this paper, mobile technologies were applied to enhance the effect of this Key-Image Mnemonics. Firstly, we reviewed background research in the fields of mobile learning and character learning of CSL/CFL learners. Second, we presented the framework and development of the app, Chinese Key-Image Learning, which aims to support autonomous learning of CFL/CSL learners. Specifically, this app provides handwriting practices with instant feedback, including handwriting demonstration, evaluation, and error correction. Finally, we conducted a focus group to gather feedback from Chinese teachers and students to understand their perceived usefulness, perceived ease of use, and perceived enjoyment after using this app. The evaluation included both quantitative and qualitative aspects. Implications for using this app in supporting character learning of CFL/CSL learners as well as our future work are discussed.
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Acknowledgements
This work was financially supported by the “Chinese Language and Technology Center” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.
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Chen, HC., Chang, LY., Tsai, MH. (2019). Chinese Key-Image Learning: An App Designed with Handwriting Evaluation and Instant Feedback to Support Chinese Character Learning. In: Rønningsbakk, L., Wu, TT., Sandnes, F., Huang, YM. (eds) Innovative Technologies and Learning. ICITL 2019. Lecture Notes in Computer Science(), vol 11937. Springer, Cham. https://doi.org/10.1007/978-3-030-35343-8_58
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